In computer vision, convolutional networks (CNNs) often adopts pooling to enlarge receptive field which has the advantage of low computational complexity. However, pooling can cause information loss ...

You can read more about sound event detection literature here. This method was first proposed in 'Sound event detection using spatial features and convolutional recurrent neural network'. It recently ...

In the encoder phase, we introduce a large receptive field context exploration (LRFC) block to extract multi-scale contextual features. The LRFC block enables the network ... separable convolutional ...

Benchmark Datasets,Class Activation Maps,Convolutional Neural Network,Edge Devices,Feature Maps,Heterogeneous Architecture,Heterogeneous Knowledge,Image Regions ...

Abstract: The aim of this research is to quickly detect cross-site scripting (XSS) attacks on the internet based on relationship diagram convolutional networks ... all node characteristics are ...

Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to ... The conversion formulas of voltage, current and power at each sampling ...

School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai, P. R. China ...

Get Instant Summarized Text (Gist) A self-attention neural network model enables rapid and accurate prediction of radiation shielding designs for space reactors, achieving less than 3% deviation ...

Taiyuan, Shanxi, China Introduction: In the aviation field, drone search and rescue is a highly urgent task involving ... Small-object detection is a long-term challenge in deep learning convolutional ...